When you're dealing with data, you have to look past the numbers.
– Nathan Yau
The famous American statistician John Tukey once said, “Exploratory data analysis can never be the whole story, but nothing else can serve as the foundation stone – as the first step.” The term exploratory data analysis is self-defining. Its simplest branch, descriptive statistics, is the methodology behind approaching and summarizing experimental data. No formal statistical training is needed for its use. Basic data manipulations such as calculating averages of experimental responses, translating data to pie charts or histograms, or assessing the variability and inspection for unusual measurements are all examples of descriptive statistics. Rather than focusing on the population using information from a sample, which is a staple of statistics, descriptive statistics is concerned with the description, summary, and presentation of the sample itself. For example, numerical summaries of a sample could be measures of location (mean, median, per-centiles, mode, extrema), measures of variability (sample standard deviation/variance, ...